Paper Info
Title
Design and Analysis of Compressed Sensing Radar Detectors
Abstract
We consider the problem of target detection from a set of Compressed Sensing (CS) radar measurements corrupted by additive white Gaussian noise. We propose two novel architectures and compare their performance by means of Receiver Operating Characteristic (ROC) curves. Using asymptotic arguments and the Complex Approximate Message Passing (CAMP) algorithm, we characterize the statistics of the $\ell_{1}$ -norm reconstruction error and derive closed form expressions for both the detection and false alarm probabilities of both schemes. Of the two architectures, we demonstrate that the best performing one consists of a reconstruction stage based on CAMP followed by a detector. This architecture, which outperforms the $\ell_{1}$-based detector in the ideal case of known background noise, can also be made fully adaptive by combining it with a conventional Constant False Alarm Rate (CFAR) processor. Using the state evolution framework of CAMP, we also derive Signal to Noise Ratio (SNR) maps that, together with the ROC curves, can be used to design a CS-based CFAR radar detector. Our theoretical findings are confirmed by means of both Monte Carlo simulations and experimental results.
Year
DOI
Venue
2013
10.1109/TSP.2012.2225057
IEEE Transactions on Signal Processing
Keywords
Field
DocType
signal reconstruction,radar,signal to noise ratio,compressed sensing,monte carlo methods,computer architecture,probability,awgn
Radar,Background noise,False alarm,Control theory,Computer science,Signal-to-noise ratio,Algorithm,Speech recognition,Constant false alarm rate,Additive white Gaussian noise,Detector,Signal reconstruction
Journal
Volume
Issue
ISSN
61
4
1053-587X
Citations 
PageRank 
References 
24
1.02
16
Authors
5
Name
Order
Citations
PageRank
Laura Anitori1783.27
Arian Maleki280357.52
Matern Otten3241.70
Richard G. Baraniuk45053489.23
Peter Hoogeboom515020.85